--- title: "High-Performance CDN Architecture and Edge Computing: Enterprise Content Delivery Guide" source: "https://support.tools/high-performance-cdn-architecture-edge-computing-enterprise-guide/" published: 2026-07-31 categories: ["Networking","Infrastructure","Edge Computing"] tags: ["CDN","Edge Computing","Performance","Caching","Networking","Infrastructure","DevOps","Enterprise"] --- Content Delivery Networks (CDN) and edge computing represent the backbone of modern internet infrastructure, enabling ultra-fast content delivery and reducing latency for global audiences. This comprehensive guide explores advanced CDN architectures, edge computing strategies, and enterprise-grade implementations for high-performance content delivery at scale. # [Advanced CDN Architecture](#advanced-cdn-architecture) ## Section 1: CDN Core Architecture and Edge Node Design Modern CDN architecture leverages distributed edge nodes, intelligent caching strategies, and advanced routing algorithms to deliver optimal performance across global networks. ### Intelligent Edge Node Implementation ```go package cdn import ( "context" "sync" "time" "net/http" "crypto/sha256" ) type EdgeNode struct { ID string Location *GeoLocation Capacity *NodeCapacity CacheManager *CacheManager OriginConnector *OriginConnector LoadBalancer *EdgeLoadBalancer SecurityEngine *SecurityEngine AnalyticsEngine *AnalyticsEngine HealthMonitor *HealthMonitor PerfOptimizer *PerformanceOptimizer mutex sync.RWMutex } type CacheManager struct { L1Cache *MemoryCache L2Cache *SSDCache L3Cache *HDDCache CachePolicy *CachePolicy PurgeManager *PurgeManager Prefetcher *ContentPrefetcher CompressionEngine *CompressionEngine HitRatio *CacheMetrics } func (e *EdgeNode) ServeContent(ctx context.Context, request *ContentRequest) (*ContentResponse, error) { e.mutex.RLock() defer e.mutex.RUnlock() // Security validation if err := e.SecurityEngine.ValidateRequest(request); err != nil { return nil, err } // Content key generation contentKey := e.generateContentKey(request) // Multi-tier cache lookup content, cacheHit := e.CacheManager.GetContent(contentKey) if cacheHit { // Cache hit - serve from cache e.AnalyticsEngine.RecordCacheHit(request, content) return e.serveFromCache(content, request) } // Cache miss - fetch from origin originContent, err := e.fetchFromOrigin(ctx, request) if err != nil { return nil, err } // Store in cache for future requests e.CacheManager.StoreContent(contentKey, originContent) // Record analytics e.AnalyticsEngine.RecordCacheMiss(request, originContent) return e.optimizeAndServe(originContent, request) } func (cm *CacheManager) GetContent(key string) (*CachedContent, bool) { // L1 Cache (Memory) - fastest access if content, found := cm.L1Cache.Get(key); found { cm.HitRatio.RecordL1Hit() return content, true } // L2 Cache (SSD) - fast access if content, found := cm.L2Cache.Get(key); found { // Promote to L1 cache cm.L1Cache.Set(key, content, cm.CachePolicy.L1TTL) cm.HitRatio.RecordL2Hit() return content, true } // L3 Cache (HDD) - slower but large capacity if content, found := cm.L3Cache.Get(key); found { // Promote to L2 and L1 caches cm.L2Cache.Set(key, content, cm.CachePolicy.L2TTL) cm.L1Cache.Set(key, content, cm.CachePolicy.L1TTL) cm.HitRatio.RecordL3Hit() return content, true } cm.HitRatio.RecordMiss() return nil, false } func (cm *CacheManager) StoreContent(key string, content *OriginContent) { cachedContent := &CachedContent{ Content: content.Data, Headers: content.Headers, ETag: content.ETag, LastModified: content.LastModified, TTL: cm.calculateTTL(content), StoredAt: time.Now(), AccessCount: 0, Size: len(content.Data), } // Intelligent cache tier selection based on content characteristics tier := cm.selectOptimalCacheTier(cachedContent) switch tier { case CacheTierL1: cm.L1Cache.Set(key, cachedContent, cm.CachePolicy.L1TTL) case CacheTierL2: cm.L2Cache.Set(key, cachedContent, cm.CachePolicy.L2TTL) // Also store in L1 if frequently accessed if cm.isFrequentlyAccessed(key) { cm.L1Cache.Set(key, cachedContent, cm.CachePolicy.L1TTL) } case CacheTierL3: cm.L3Cache.Set(key, cachedContent, cm.CachePolicy.L3TTL) } } func (cm *CacheManager) selectOptimalCacheTier(content *CachedContent) CacheTier { // Decision matrix based on content characteristics if content.Size < 1024*1024 && content.IsFrequentlyRequested() { return CacheTierL1 // Small, hot content in memory } if content.Size < 100*1024*1024 && content.IsModeratelyRequested() { return CacheTierL2 // Medium content on SSD } return CacheTierL3 // Large or infrequent content on HDD } // Advanced Content Prefetching type ContentPrefetcher struct { MLPredictor *MachineLearningPredictor PatternAnalyzer *RequestPatternAnalyzer PrefetchQueue *PriorityQueue BandwidthManager *BandwidthManager } func (cp *ContentPrefetcher) PredictAndPrefetch(ctx context.Context) { for { select { case <-ctx.Done(): return case <-time.After(30 * time.Second): cp.executePrefetchCycle() } } } func (cp *ContentPrefetcher) executePrefetchCycle() { // Analyze request patterns patterns := cp.PatternAnalyzer.AnalyzeRecentPatterns() // Predict likely requests predictions := cp.MLPredictor.PredictLikelyRequests(patterns) // Filter by confidence and available bandwidth viablePredictions := cp.filterViablePredictions(predictions) // Execute prefetch operations for _, prediction := range viablePredictions { if cp.BandwidthManager.CanAllocateBandwidth(prediction.EstimatedSize) { go cp.prefetchContent(prediction) } } } func (cp *ContentPrefetcher) prefetchContent(prediction *ContentPrediction) { // Allocate bandwidth cp.BandwidthManager.AllocateBandwidth(prediction.EstimatedSize) defer cp.BandwidthManager.ReleaseBandwidth(prediction.EstimatedSize) // Fetch content from origin content, err := cp.fetchContentFromOrigin(prediction.URL) if err != nil { return } // Store in appropriate cache tier cp.storeInCache(prediction.URL, content) } ``` ## Section 2: Advanced Caching Strategies Implementing sophisticated caching strategies that maximize hit ratios while minimizing storage costs and origin load. ### Intelligent Cache Replacement Policies ```python class AdvancedCachePolicy: def __init__(self): self.lru_policy = LRUPolicy() self.lfu_policy = LFUPolicy() self.arc_policy = ARCPolicy() self.ml_policy = MLCachePolicy() self.hybrid_policy = HybridCachePolicy() def select_eviction_candidate(self, cache_tier, required_space): """Select optimal cache eviction candidate using multiple policies""" candidates = { 'lru': self.lru_policy.get_eviction_candidate(cache_tier), 'lfu': self.lfu_policy.get_eviction_candidate(cache_tier), 'arc': self.arc_policy.get_eviction_candidate(cache_tier), 'ml': self.ml_policy.get_eviction_candidate(cache_tier), 'hybrid': self.hybrid_policy.get_eviction_candidate(cache_tier) } # Score each candidate best_candidate = None best_score = float('inf') for policy_name, candidate in candidates.items(): if candidate: score = self.calculate_eviction_score(candidate, policy_name) if score < best_score: best_score = score best_candidate = candidate return best_candidate def calculate_eviction_score(self, candidate, policy_name): """Calculate composite eviction score""" weights = { 'lru': 0.2, 'lfu': 0.2, 'arc': 0.2, 'ml': 0.3, 'hybrid': 0.1 } # Base score from policy base_score = candidate.policy_score # Adjust for content characteristics content_factor = self.calculate_content_factor(candidate) # Adjust for business value business_factor = self.calculate_business_factor(candidate) # Adjust for cost considerations cost_factor = self.calculate_cost_factor(candidate) composite_score = (base_score * content_factor * business_factor * cost_factor * weights[policy_name]) return composite_score class ARCPolicy: """Adaptive Replacement Cache policy implementation""" def __init__(self, cache_size): self.cache_size = cache_size self.p = 0 # Target size for T1 self.t1 = OrderedDict() # Recently used pages self.t2 = OrderedDict() # Frequently used pages self.b1 = OrderedDict() # Ghost list for T1 self.b2 = OrderedDict() # Ghost list for T2 def access(self, key, content): """Process cache access using ARC algorithm""" if key in self.t1: # Hit in T1 - move to T2 del self.t1[key] self.t2[key] = content return content if key in self.t2: # Hit in T2 - move to end del self.t2[key] self.t2[key] = content return content if key in self.b1: # Hit in B1 - adapt and move to T2 self.adapt(len(self.b1), len(self.b2)) self.replace(key) del self.b1[key] self.t2[key] = content return None if key in self.b2: # Hit in B2 - adapt and move to T2 self.adapt(len(self.b1), len(self.b2)) self.replace(key) del self.b2[key] self.t2[key] = content return None # Cache miss if len(self.t1) + len(self.b1) == self.cache_size: if len(self.t1) < self.cache_size: # Remove from B1 self.b1.popitem(last=False) self.replace(key) else: # Remove from T1 self.t1.popitem(last=False) elif len(self.t1) + len(self.t2) + len(self.b1) + len(self.b2) >= self.cache_size: if len(self.t1) + len(self.t2) + len(self.b1) + len(self.b2) == 2 * self.cache_size: # Remove from B2 self.b2.popitem(last=False) self.replace(key) self.t1[key] = content return None def adapt(self, b1_size, b2_size): """Adapt the target size for T1""" if b1_size >= b2_size: self.p = min(self.cache_size, self.p + max(1, b2_size / b1_size)) else: self.p = max(0, self.p - max(1, b1_size / b2_size)) def replace(self, key): """Replace a page according to ARC policy""" if len(self.t1) >= 1 and ((key in self.b2 and len(self.t1) == self.p) or len(self.t1) > self.p): # Move from T1 to B1 old_key = next(iter(self.t1)) old_content = self.t1.pop(old_key) self.b1[old_key] = None else: # Move from T2 to B2 old_key = next(iter(self.t2)) old_content = self.t2.pop(old_key) self.b2[old_key] = None class MLCachePolicy: """Machine Learning-based cache policy""" def __init__(self): self.feature_extractor = FeatureExtractor() self.predictor = CachePredictor() self.feedback_collector = FeedbackCollector() def predict_future_access(self, content_items): """Predict future access patterns using ML""" features = [] for item in content_items: feature_vector = self.feature_extractor.extract_features(item) features.append(feature_vector) # Predict access probabilities access_predictions = self.predictor.predict_access_probability(features) return access_predictions def get_eviction_candidate(self, cache_tier): """Select eviction candidate using ML predictions""" content_items = cache_tier.get_all_items() # Get ML predictions for future access predictions = self.predict_future_access(content_items) # Find item with lowest predicted access probability min_probability = float('inf') eviction_candidate = None for i, item in enumerate(content_items): predicted_access = predictions[i] # Consider multiple factors in addition to access probability composite_score = self.calculate_ml_score(item, predicted_access) if composite_score < min_probability: min_probability = composite_score eviction_candidate = item return eviction_candidate def calculate_ml_score(self, item, predicted_access): """Calculate composite ML-based score for eviction decision""" # Base score from ML prediction ml_score = 1.0 - predicted_access # Adjust for content size (prefer evicting larger items) size_factor = min(2.0, item.size / (1024 * 1024)) # MB # Adjust for age (prefer evicting older items) age_factor = min(2.0, (time.time() - item.last_access) / 3600) # hours # Adjust for access frequency frequency_factor = 1.0 / max(1, item.access_count) composite_score = ml_score * size_factor * age_factor * frequency_factor return composite_score class FeatureExtractor: def extract_features(self, content_item): """Extract features for ML cache prediction""" features = { # Temporal features 'hour_of_day': time.localtime().tm_hour, 'day_of_week': time.localtime().tm_wday, 'time_since_last_access': time.time() - content_item.last_access, 'time_since_creation': time.time() - content_item.created_at, # Content characteristics 'content_size': content_item.size, 'content_type': self.encode_content_type(content_item.mime_type), 'compression_ratio': content_item.compression_ratio, # Access patterns 'access_count': content_item.access_count, 'access_frequency': content_item.access_count / max(1, (time.time() - content_item.created_at) / 3600), 'unique_client_count': len(content_item.unique_clients), # Geographic features 'request_geographic_spread': content_item.geographic_spread, 'primary_region': self.encode_region(content_item.primary_region), # Business features 'content_priority': content_item.business_priority, 'customer_tier': content_item.customer_tier, 'revenue_impact': content_item.revenue_impact } return list(features.values()) ``` ## Section 3: Edge Computing and Serverless Functions Implementing edge computing capabilities that bring computation closer to users for ultra-low latency applications. ### Edge Function Runtime ```go package edge import ( "context" "fmt" "time" "sync" ) type EdgeFunctionRuntime struct { Functions map[string]*EdgeFunction ResourceManager *ResourceManager SecurityManager *SecurityManager MonitoringAgent *MonitoringAgent ScalingManager *ScalingManager CodeCache *CodeCache mutex sync.RWMutex } type EdgeFunction struct { ID string Name string Runtime RuntimeType Code []byte Config *FunctionConfig Resources *ResourceAllocation Metrics *FunctionMetrics WarmInstances []*FunctionInstance ColdInstances []*FunctionInstance LastDeployment time.Time } type FunctionInstance struct { ID string Runtime *IsolatedRuntime State InstanceState LastUsed time.Time RequestCount int64 MemoryUsage int64 CPUUsage float64 StartupTime time.Duration } func (efr *EdgeFunctionRuntime) ExecuteFunction(ctx context.Context, functionID string, request *EdgeRequest) (*EdgeResponse, error) { efr.mutex.RLock() function, exists := efr.Functions[functionID] efr.mutex.RUnlock() if !exists { return nil, fmt.Errorf("function %s not found", functionID) } // Get or create function instance instance, err := efr.getOrCreateInstance(function) if err != nil { return nil, err } // Security validation if err := efr.SecurityManager.ValidateExecution(function, request); err != nil { return nil, err } // Execute function with timeout executionCtx, cancel := context.WithTimeout(ctx, function.Config.Timeout) defer cancel() startTime := time.Now() response, err := instance.Execute(executionCtx, request) executionTime := time.Since(startTime) // Update metrics efr.updateExecutionMetrics(function, instance, executionTime, err) // Return instance to pool or terminate efr.returnInstance(function, instance) return response, err } func (efr *EdgeFunctionRuntime) getOrCreateInstance(function *EdgeFunction) (*FunctionInstance, error) { // Try to get warm instance first if len(function.WarmInstances) > 0 { instance := function.WarmInstances[0] function.WarmInstances = function.WarmInstances[1:] return instance, nil } // Check if we can create new instance if !efr.ResourceManager.CanAllocateResources(function.Resources) { return nil, fmt.Errorf("insufficient resources") } // Create new instance (cold start) instance, err := efr.createNewInstance(function) if err != nil { return nil, err } return instance, nil } func (efr *EdgeFunctionRuntime) createNewInstance(function *EdgeFunction) (*FunctionInstance, error) { // Allocate resources allocation, err := efr.ResourceManager.AllocateResources(function.Resources) if err != nil { return nil, err } // Create isolated runtime runtime, err := efr.createIsolatedRuntime(function, allocation) if err != nil { efr.ResourceManager.ReleaseResources(allocation) return nil, err } // Load function code if err := runtime.LoadCode(function.Code); err != nil { runtime.Terminate() efr.ResourceManager.ReleaseResources(allocation) return nil, err } instance := &FunctionInstance{ ID: generateInstanceID(), Runtime: runtime, State: InstanceStateInitializing, LastUsed: time.Now(), RequestCount: 0, } // Initialize function if err := instance.Initialize(); err != nil { instance.Terminate() return nil, err } instance.State = InstanceStateReady return instance, nil } func (efr *EdgeFunctionRuntime) createIsolatedRuntime(function *EdgeFunction, allocation *ResourceAllocation) (*IsolatedRuntime, error) { switch function.Runtime { case RuntimeJavaScript: return NewV8Runtime(allocation) case RuntimeWebAssembly: return NewWASMRuntime(allocation) case RuntimePython: return NewPythonRuntime(allocation) case RuntimeGo: return NewGoRuntime(allocation) default: return nil, fmt.Errorf("unsupported runtime: %s", function.Runtime) } } // WebAssembly Runtime Implementation type WASMRuntime struct { Module *wasmtime.Module Store *wasmtime.Store Instance *wasmtime.Instance Memory *wasmtime.Memory ResourceLimits *ResourceAllocation StartTime time.Time } func NewWASMRuntime(allocation *ResourceAllocation) (*WASMRuntime, error) { engine := wasmtime.NewEngine() store := wasmtime.NewStore(engine) // Configure resource limits store.SetEpochDeadline(uint64(allocation.MaxExecutionTime.Nanoseconds())) return &WASMRuntime{ Store: store, ResourceLimits: allocation, StartTime: time.Now(), }, nil } func (wr *WASMRuntime) LoadCode(code []byte) error { module, err := wasmtime.NewModule(wr.Store.Engine, code) if err != nil { return err } wr.Module = module // Create instance with imports imports := wr.createImports() instance, err := wasmtime.NewInstance(wr.Store, wr.Module, imports) if err != nil { return err } wr.Instance = instance // Get memory export memoryExport := wr.Instance.GetExport(wr.Store, "memory") if memoryExport != nil { wr.Memory = memoryExport.Memory() } return nil } func (wr *WASMRuntime) Execute(ctx context.Context, request *EdgeRequest) (*EdgeResponse, error) { // Get main function mainFunc := wr.Instance.GetFunc(wr.Store, "main") if mainFunc == nil { return nil, fmt.Errorf("main function not found") } // Serialize request requestData, err := json.Marshal(request) if err != nil { return nil, err } // Write request to WASM memory requestPtr, err := wr.allocateMemory(len(requestData)) if err != nil { return nil, err } copy(wr.Memory.Data(wr.Store)[requestPtr:], requestData) // Execute function result, err := mainFunc.Call(wr.Store, requestPtr, len(requestData)) if err != nil { return nil, err } // Read response from WASM memory responsePtr := result.(int32) responseData := wr.readFromMemory(responsePtr) // Deserialize response var response EdgeResponse if err := json.Unmarshal(responseData, &response); err != nil { return nil, err } return &response, nil } // Edge Function Auto-scaling type EdgeFunctionScaler struct { MetricsCollector *MetricsCollector ScalingPolicies map[string]*ScalingPolicy InstanceManager *InstanceManager } func (efs *EdgeFunctionScaler) MonitorAndScale() { ticker := time.NewTicker(10 * time.Second) defer ticker.Stop() for range ticker.C { efs.evaluateScalingDecisions() } } func (efs *EdgeFunctionScaler) evaluateScalingDecisions() { for functionID, policy := range efs.ScalingPolicies { metrics := efs.MetricsCollector.GetFunctionMetrics(functionID) scalingDecision := efs.makeScalingDecision(metrics, policy) if scalingDecision.ShouldScale() { efs.executeScaling(functionID, scalingDecision) } } } func (efs *EdgeFunctionScaler) makeScalingDecision(metrics *FunctionMetrics, policy *ScalingPolicy) *ScalingDecision { decision := &ScalingDecision{ FunctionID: metrics.FunctionID, CurrentInstances: metrics.ActiveInstances, } // Evaluate scale-up conditions if metrics.AverageLatency > policy.LatencyThreshold || metrics.RequestRate > policy.RequestRateThreshold || metrics.QueueDepth > policy.QueueDepthThreshold { targetInstances := efs.calculateTargetInstances(metrics, policy) if targetInstances > decision.CurrentInstances { decision.Action = ScaleUp decision.TargetInstances = targetInstances } } // Evaluate scale-down conditions if metrics.AverageLatency < policy.LatencyThreshold * 0.5 && metrics.RequestRate < policy.RequestRateThreshold * 0.3 && decision.CurrentInstances > policy.MinInstances { targetInstances := max(policy.MinInstances, decision.CurrentInstances - 1) decision.Action = ScaleDown decision.TargetInstances = targetInstances } return decision } ``` ## Section 4: Global Load Balancing and Traffic Steering Implementing intelligent global load balancing that considers multiple factors for optimal traffic distribution. ### Geographic Traffic Steering ```python class GlobalLoadBalancer: def __init__(self): self.geo_ip_database = GeoIPDatabase() self.latency_matrix = GlobalLatencyMatrix() self.capacity_monitor = CapacityMonitor() self.health_monitor = GlobalHealthMonitor() self.traffic_policies = TrafficPolicyEngine() def route_request(self, request): """Route request to optimal edge location""" # Determine client location client_location = self.geo_ip_database.get_location(request.client_ip) # Get available edge locations available_edges = self.get_available_edge_locations() # Apply traffic policies eligible_edges = self.traffic_policies.filter_edges( available_edges, request, client_location ) # Calculate routing scores edge_scores = {} for edge in eligible_edges: score = self.calculate_routing_score( edge, client_location, request ) edge_scores[edge] = score # Select best edge location best_edge = max(edge_scores, key=edge_scores.get) return best_edge def calculate_routing_score(self, edge, client_location, request): """Calculate composite routing score for edge location""" weights = { 'latency': 0.4, 'capacity': 0.3, 'health': 0.2, 'cost': 0.1 } # Latency score (lower latency = higher score) latency = self.latency_matrix.get_latency(client_location, edge.location) latency_score = max(0, 100 - latency) # Capacity score capacity_utilization = self.capacity_monitor.get_utilization(edge) capacity_score = max(0, 100 - capacity_utilization) # Health score health_status = self.health_monitor.get_health_score(edge) # Cost score (lower cost = higher score) cost_factor = self.calculate_cost_factor(edge, request) cost_score = max(0, 100 - cost_factor) # Composite score composite_score = ( weights['latency'] * latency_score + weights['capacity'] * capacity_score + weights['health'] * health_status + weights['cost'] * cost_score ) return composite_score class TrafficPolicyEngine: def __init__(self): self.policies = {} self.rule_engine = PolicyRuleEngine() def add_policy(self, policy_name, policy_config): """Add traffic steering policy""" policy = TrafficPolicy( name=policy_name, rules=policy_config.rules, conditions=policy_config.conditions, actions=policy_config.actions ) self.policies[policy_name] = policy def filter_edges(self, edges, request, client_location): """Filter edge locations based on traffic policies""" eligible_edges = edges.copy() for policy_name, policy in self.policies.items(): if policy.applies_to_request(request): eligible_edges = policy.filter_edges( eligible_edges, request, client_location ) return eligible_edges class AdvancedTrafficSteering: def __init__(self): self.ml_predictor = TrafficPredictor() self.anomaly_detector = TrafficAnomalyDetector() self.cost_optimizer = CostOptimizer() def intelligent_traffic_steering(self, request, available_edges): """Implement AI-driven traffic steering""" # Predict traffic patterns traffic_prediction = self.ml_predictor.predict_traffic_patterns( request, available_edges ) # Detect traffic anomalies anomalies = self.anomaly_detector.detect_anomalies(request) # Optimize for cost efficiency cost_optimization = self.cost_optimizer.optimize_routing( request, available_edges ) # Make intelligent routing decision routing_decision = self.make_intelligent_decision( traffic_prediction, anomalies, cost_optimization ) return routing_decision def make_intelligent_decision(self, prediction, anomalies, cost_opt): """Make intelligent routing decision using ML""" decision_factors = { 'predicted_performance': prediction.performance_score, 'anomaly_risk': anomalies.risk_score, 'cost_efficiency': cost_opt.efficiency_score, 'resource_availability': prediction.resource_availability } # Use ML model to make final decision optimal_edge = self.ml_predictor.select_optimal_edge( decision_factors ) return optimal_edge class CDNPerformanceOptimizer: def __init__(self): self.compression_optimizer = CompressionOptimizer() self.image_optimizer = ImageOptimizer() self.protocol_optimizer = ProtocolOptimizer() self.bandwidth_optimizer = BandwidthOptimizer() def optimize_content_delivery(self, content, request): """Optimize content for delivery""" optimized_content = content # Compression optimization if self.should_compress(content, request): optimized_content = self.compression_optimizer.optimize( optimized_content, request ) # Image optimization if content.is_image(): optimized_content = self.image_optimizer.optimize( optimized_content, request ) # Protocol optimization protocol = self.protocol_optimizer.select_optimal_protocol(request) # Bandwidth optimization optimized_content = self.bandwidth_optimizer.optimize_for_bandwidth( optimized_content, request.connection_speed ) return optimized_content, protocol def should_compress(self, content, request): """Determine if content should be compressed""" # Don't compress already compressed content if content.is_already_compressed(): return False # Don't compress small files (compression overhead) if content.size < 1024: # 1KB return False # Consider client capabilities if not request.supports_compression(): return False # Consider content type compressible_types = [ 'text/html', 'text/css', 'text/javascript', 'application/json', 'application/xml' ] return content.mime_type in compressible_types class EdgeCacheInvalidation: def __init__(self): self.invalidation_strategies = { 'immediate': ImmediateInvalidation(), 'progressive': ProgressiveInvalidation(), 'smart': SmartInvalidation(), 'scheduled': ScheduledInvalidation() } def invalidate_content(self, content_pattern, strategy='smart'): """Invalidate content across edge locations""" invalidation_strategy = self.invalidation_strategies[strategy] # Find affected edge locations affected_edges = self.find_affected_edges(content_pattern) # Execute invalidation results = invalidation_strategy.execute( content_pattern, affected_edges ) return results def find_affected_edges(self, content_pattern): """Find edge locations that have the content cached""" affected_edges = [] for edge in self.get_all_edge_locations(): if edge.cache_manager.has_content(content_pattern): affected_edges.append(edge) return affected_edges class SmartInvalidation: def execute(self, content_pattern, affected_edges): """Smart invalidation based on usage patterns""" invalidation_plan = self.create_invalidation_plan( content_pattern, affected_edges ) results = {} for edge, plan in invalidation_plan.items(): if plan.immediate: # Immediate invalidation for high-traffic edges result = self.immediate_invalidate(edge, content_pattern) else: # Lazy invalidation for low-traffic edges result = self.lazy_invalidate(edge, content_pattern) results[edge.id] = result return results def create_invalidation_plan(self, content_pattern, affected_edges): """Create intelligent invalidation plan""" plan = {} for edge in affected_edges: # Analyze traffic patterns traffic_analysis = self.analyze_edge_traffic(edge, content_pattern) # Determine invalidation strategy if traffic_analysis.is_high_traffic(): plan[edge] = InvalidationPlan(immediate=True) else: plan[edge] = InvalidationPlan(immediate=False, ttl_override=300) return plan ``` This comprehensive guide demonstrates enterprise-grade CDN architecture with intelligent edge computing, advanced caching strategies, global load balancing, and performance optimization techniques. The examples provide production-ready patterns for building high-performance content delivery networks that can handle massive global traffic while maintaining optimal performance and cost efficiency.